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Don't make the mistake of underestimating the power of kmeans clustering!

The kmeans clustering algorithm is designed to mine hidden patterns in unlabeled data.

Since most of the world's data is unlabeled, kmeans cluster analysis is useful to any professional.

The good news is that kmeans is as easy to learn as it is powerful. This crash course will prove it to you.

You will also learn how to use the scikitlearn library in Python to perform kmeans clustering.


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Book links

Intro to Statistical Learning:
https://www.statlearning.com/

Data Mining textbook:
https://www.amazon.com/IntroductionM...



Video Chapters

00:00 Intro
01:30 What is Cluster Analysis?
04:13 Why Cluster Analysis?
06:50 The Challenges of Clustering
10:57 The Dataset
16:21 Hierarchical Clustering
19:00 Partitional Clustering
19:43 Overlapping Clustering
21:35 Complete vs. Partial Clustering
23:06 WellSeparated Clusters
24:13 Prototype Clusters
27:30 DensityBased Clusters
30:08 Evaluating Your Clusters
32:44 KMeans Clustering Algorithm
42:53 Euclidian Distance
46:21 KMeans with Python Code
55:31 KMeans Caveats
58:36 Interpreting Clusters
01:03:11 Continue Your Learning

#machinelearning #kmeans #datascience